{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "83f5001c-4f04-466b-b942-ef2d3800f8b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据预处理完成，准备进行模型训练和测试。\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('./combined_data.csv')\n",
    "\n",
    "# 删除不需要的列，例如时间戳或IP地址（假设你的数据集中有这些列）\n",
    "data.drop([' Timestamp'], axis=1, inplace=True)\n",
    "\n",
    "# 类型转换，将分类标签编码\n",
    "label_encoder = LabelEncoder()\n",
    "data[' Label'] = label_encoder.fit_transform(data[' Label'])\n",
    "\n",
    "# 检查并处理无穷大和非常大的数值\n",
    "data.replace([np.inf, -np.inf], np.nan, inplace=True)  # 将inf替换为NaN\n",
    "data.fillna(data.median(), inplace=True)  # 使用中位数填充NaN，确保之前中位数计算不包括inf\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(data.drop(' Label', axis=1))  # 确保标签列不参与标准化\n",
    "y = data[' Label']\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "print(\"数据预处理完成，准备进行模型训练和测试。\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3cb72e1-33a8-4fa6-92ad-eed5ab041fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "# 初始化模型\n",
    "logreg = LogisticRegression(max_iter=1000)\n",
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "svm = SVC()\n",
    "\n",
    "# 训练逻辑回归模型\n",
    "logreg.fit(X_train, y_train)\n",
    "y_pred_logreg = logreg.predict(X_test)\n",
    "logreg_accuracy = accuracy_score(y_test, y_pred_logreg) * 100\n",
    "\n",
    "# 训练随机森林模型\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "rf_accuracy = accuracy_score(y_test, y_pred_rf) * 100\n",
    "\n",
    "# 训练支持向量机模型\n",
    "svm.fit(X_train, y_train)\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "svm_accuracy = accuracy_score(y_test, y_pred_svm) * 100\n",
    "\n",
    "# # 训练XGBoost模型\n",
    "# xgb.fit(X_train, y_train)\n",
    "# y_pred_xgb = xgb.predict(X_test)\n",
    "# xgb_accuracy = accuracy_score(y_test, y_pred_xgb) * 100\n",
    "\n",
    "# 模型名称和准确度列表\n",
    "models = ['Logistic Regression', 'Random Forest', 'SVM']  #, 'XGBoost'\n",
    "accuracies = [logreg_accuracy, rf_accuracy, svm_accuracy]  #, xgb_accuracy\n",
    "\n",
    "# 绘制折线图\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(models, accuracies, marker='o', linestyle='-', color='b')\n",
    "plt.title('Accuracy of Different Classification Models')\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.grid(True)\n",
    "plt.xticks(models)\n",
    "plt.ylim([min(accuracies) - 5, max(accuracies) + 5])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4978d75-3b7d-49ca-93a1-7c84b1e99bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from xgboost import XGBClassifier\n",
    "# from sklearn.metrics import accuracy_score\n",
    "# import numpy as np\n",
    "\n",
    "# # 初始化XGBoost模型\n",
    "# xgb = XGBClassifier(n_estimators=100, use_label_encoder=False, eval_metric='mlogloss')\n",
    "\n",
    "# # 训练模型\n",
    "# eval_set = [(X_train, y_train), (X_test, y_test)]\n",
    "# xgb.fit(X_train, y_train, eval_set=eval_set, verbose=10, early_stopping_rounds=10)\n",
    "\n",
    "# # 获取每10轮的测试集结果\n",
    "# results = xgb.evals_result()\n",
    "# epochs = len(results['validation_1']['mlogloss'])\n",
    "# x_axis = range(0, epochs, 10)  # 每10轮作为一个检查点\n",
    "\n",
    "# # 绘制mlogloss的变化\n",
    "# fig, ax = plt.subplots()\n",
    "# ax.plot(x_axis, results['validation_1']['mlogloss'][0::10], label='Test')\n",
    "# ax.legend()\n",
    "# plt.ylabel('Log Loss')\n",
    "# plt.title('XGBoost Log Loss')\n",
    "# plt.show()\n",
    "\n",
    "# # 打印最后的准确率\n",
    "# y_pred = xgb.predict(X_test)\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "# print(f\"Final Accuracy: {accuracy:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed08b466-38a2-4286-bac5-a14ba7d02440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "# 初始化模型\n",
    "logreg = LogisticRegression(max_iter=1000)\n",
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "svm = SVC()\n",
    "\n",
    "# 训练模型\n",
    "logreg.fit(X_train, y_train)\n",
    "rf.fit(X_train, y_train)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# 预测结果\n",
    "y_pred_logreg = logreg.predict(X_test)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "\n",
    "# 混淆矩阵\n",
    "cm_logreg = confusion_matrix(y_test, y_pred_logreg)\n",
    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# 绘制混淆矩阵的热图\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
    "sns.heatmap(cm_logreg, annot=True, fmt=\"d\", ax=axes[0], cmap='Blues')\n",
    "axes[0].set_title('Logistic Regression Confusion Matrix')\n",
    "axes[0].set_xlabel('Predicted labels')\n",
    "axes[0].set_ylabel('True labels')\n",
    "\n",
    "sns.heatmap(cm_rf, annot=True, fmt=\"d\", ax=axes[1], cmap='Blues')\n",
    "axes[1].set_title('Random Forest Confusion Matrix')\n",
    "axes[1].set_xlabel('Predicted labels')\n",
    "axes[1].set_ylabel('True labels')\n",
    "\n",
    "sns.heatmap(cm_svm, annot=True, fmt=\"d\", ax=axes[2], cmap='Blues')\n",
    "axes[2].set_title('SVM Confusion Matrix')\n",
    "axes[2].set_xlabel('Predicted labels')\n",
    "axes[2].set_ylabel('True labels')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b50f7c2-f71d-4ae8-a5c7-4be8dc1d2c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "# 初始化模型\n",
    "logreg = LogisticRegression(max_iter=1000)\n",
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "svm = SVC()\n",
    "\n",
    "# 训练模型\n",
    "logreg.fit(X_train, y_train)\n",
    "rf.fit(X_train, y_train)\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# 预测结果\n",
    "y_pred_logreg = logreg.predict(X_test)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "\n",
    "# 混淆矩阵\n",
    "cm_logreg = confusion_matrix(y_test, y_pred_logreg)\n",
    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# 绘制混淆矩阵的热图\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
    "sns.heatmap(cm_logreg, annot=True, fmt=\"d\", ax=axes[0], cmap='Blues')\n",
    "axes[0].set_title('Logistic Regression Confusion Matrix')\n",
    "axes[0].set_xlabel('Predicted labels')\n",
    "axes[0].set_ylabel('True labels')\n",
    "\n",
    "sns.heatmap(cm_rf, annot=True, fmt=\"d\", ax=axes[1], cmap='Blues')\n",
    "axes[1].set_title('Random Forest Confusion Matrix')\n",
    "axes[1].set_xlabel('Predicted labels')\n",
    "axes[1].set_ylabel('True labels')\n",
    "\n",
    "sns.heatmap(cm_svm, annot=True, fmt=\"d\", ax=axes[2], cmap='Blues')\n",
    "axes[2].set_title('SVM Confusion Matrix')\n",
    "axes[2].set_xlabel('Predicted labels')\n",
    "axes[2].set_ylabel('True labels')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
